FieldAI Benefits Overview

Build and operate scalable data-platform systems for multimodal robotics and machine-learning datasets. Responsibilities include ingestion, processing, validation, labeling, dataset generation, metadata, lineage, versioning, data quality, observability, and workflow reliability. Partner with ML, robotics, infrastructure, and labeling teams to create reusable platform capabilities supporting large-scale data processing, reprocessing, backfills, training, and evaluation.

Build and automate data-flywheel systems for autonomous robots, converting large-scale robot experiences into high-quality, versioned training datasets. Responsibilities include data processing, filtering, labeling, auto-labeling, offline inference, hard-example selection, validation, reproducible dataset generation, and incremental training-data pipelines. Partner with researchers and platform teams to productionize experimental workflows and improve the cycle from robot deployment to usable ML data.

Architect and develop high-fidelity multi-robot simulation environments and fleet orchestration systems. Build realistic physics, perception, communications, task allocation, planning, scenario generation, domain randomization, metrics, and experiment infrastructure. Calibrate simulations against real-world telemetry, automate regression benchmarks, and collaborate with robotics, infrastructure, DevOps, and field teams to deliver reliable, scalable, field-ready systems.